Recommendation Types

We support several different types of recommendations, taking into account individual user behavior and aggregate behavior over all users. See below for the common list of recommendations that are available in Recommendation Studio and descriptions on how they are computed. By default, all recommendations are refreshed once daily.

Site-wide recommendations rank catalog items based on all user activity across desktop sites and mobile apps and are based on events sent to Blueshift. 

 

Name Description Default parameters
Top viewed Ranked list of catalog items sorted by how frequently they were viewed in the last 28 days lookback_window : 28 days
event : view
Top selling Ranked list of catalog items sorted by how frequently they were bought in the last 28 days lookback_window : 28 days
event : purchase
Top wishlisted/downloaded/.... Ranked list of catalog items sorted by how frequently they were sent in specific custom event in the last 28 days. For example, event might be add_to_wishlist or download  lookback_window : 28 days
event : any custom event with product_ids
 

2. Category Recommendations (Lite)

Category recommendations rank catalog items based on all user activity across desktop sites and mobile apps and are based on events sent to Blueshift for each category. If your category hierarchy is multi level the aggregates are computed at each level of the hierarchy. For example, if your category is similar to the below example,  then category recommendations are computed at each of the 3 levels.   i.e Travel, Travel > Europe and Travel > Europe > Italy. In Recommendation Studio we define top level to be "Root" category and bottom most level to be "Leaf" category and one immediately above Leaf to be "Parent" category.

 
Name Description Default parameters
Top viewed Ranked list of catalog items in this category sorted by how frequently they were viewed in the last 28 days lookback_window : 28 days
event : view
Top selling Ranked list of catalog items in this category sorted by how frequently they were bought in the last 28 days lookback_window : 28 days
event : purchase
Top wishlisted/downloaded/.... Ranked list of catalog items in this category sorted by how frequently they were sent in specific custom event in the last 28 days. For example, the event might be add_to_wishlist or download lookback_window : 28 days
event : any custom event with product_ids
Lowest price Ranked list of catalog items in this category sorted by price in ascending order price > 0
Recent additions Ranked list of catalog items in this category sorted on recency based on start_date attribute i.e (start_date - now) ascending order start_date IS NOT NULL
Expiring soon Ranked list of catalog items in this category sorted on how soon they are expiring i.e (end_date - now) ascending order end_date IS NOT NULL
Top discounted Ranked list of catalog items in this category sorted on discount percentage i.e (msrp-price)/msrp in descending order price > 0
msrp > 0
msrp IS NOT NULL 

 

 

Category recommendations are one type of product attribute recommendations.  Thus, the above can be computed for any other product attribute like brand, author, gender, location, language etc

 

3. Product Recommendations (Full)

Product to product recommendations rank items that are related to or likely to be of interest as in next best product to consider. For example, people who view this product also view these other products will rank items based on how frequently they were viewed across all user sessions.

 

Name Description Default parameters
Next Best Product - view to view Ranked list of viewed items after viewing this item based on collaborative filtering. The ranking is based on how often this occurs across all user sessions in given window of time. lookback_window : 91 days
event : view
Next Best Product - view to purchase Ranked list of purchased items after viewing this item. The ranking is based on how often this occurs across all user sessions in given window of time. lookback_window : 91 days
events : view, purchase
Next Best Product - purchase to purchase Ranked list of items who go on to purchase other items after purchasing this item. The ranking is based on how often this occurs across all users in given window of time. lookback_window : 91 days
event : purchase
Next Best Product - custom event to custom event Ranked list of items for this custom event after having done this other event. The ranking is based on how often this occurs across all users in given window of time. lookback_window : 91 days
events : any pair of custom events likely to happen together. For example add_to_wishlist and download
Next Best Product - geo distance Ranked list of catalog items closest in geo distance to this product/item latitude IS NOT NULL
longitude IS NOT NULL
Next Best Product - text similarity Ranked list of catalog items similar to this product/item in text similarity based on title and tags  title IS NOT NULL, tags IS NOT NULL

 

 
 

4. User Recommendations  (Full)

User recommendations are computed based on individual user activity and are ideal for use in daily or weekly newsletters.

Name  Description Default parameteres
Top timespent items/products Ranked list of items for each user sorted on how long that user spent time viewing or interacting this item. We use exponential decay to weight the most recent ones above those in the past lookback_window : 14 days
Top timespent categories Ranked list of categories for each user sorted on how long that user spent time viewing or interacting this category. We use exponential decay to weight the most recent ones above those in the past lookback_window : 14 days
Top affinity categories Ranked list of categories for each user sorted on how frequently that user has been interacting with this category across all events  lookback_window : 91 days

 

 

 

Blueshift allows a marketer to fully customize each of the above recommendations to suit their campaign needs. Contact your Blueshift Success Manager to set them up in your account. Once set up, they will be available as drop-down options in the Recommendation Studio.

 

5. Custom Imports (Full)

You can also import existing recommendations into the platform. This allows you to mix and match imported ones with platform defaults. The feeds are imported via direct file uploads, S3/FTP folders or HTTP end points. We support importing the following types of recommendations:

   
Type Description Comments
User to Product Recommendations that go from a single user to an array of products A CSV file with two columns, user identifier like customer_id or email AND product_ids separated by a delimiter
Product to Product Recommendations that go from a single product to an array of products  A CSV file with two columns, source_product_id AND destination_product_ids

 

 

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